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Using a deep learning network to recognise low back pain in static standing.

Boyi Hu1, Chong Kim2, Xiaopeng Ning3

  • 1a Department of Environmental Health , Harvard T.H. Chan School of Public Health , Boston , MA , USA.

Ergonomics
|May 25, 2018
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Summary
This summary is machine-generated.

Deep neural networks can identify individuals with low back pain (LBP) using balance data. This artificial intelligence approach achieved high precision and recall, aiding in LBP detection from kinematic data.

Keywords:
Low back painbalance controldeep neural networklong-short-term memorymotion analysis

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Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Musculoskeletal Disorders

Background:

  • Low back pain (LBP) is a leading cause of disability globally.
  • Current diagnostic methods for LBP often lack objective measures.
  • Algorithms for recognizing LBP patients using balance data are scarce.

Purpose of the Study:

  • To investigate the feasibility of using deep neural networks (DNNs) to identify chronic low back pain (LBP) populations.
  • To utilize human balance and body sway performance data for LBP recognition.
  • To develop and evaluate an AI model for classifying LBP patients from healthy individuals.

Main Methods:

  • 44 individuals with chronic LBP and healthy controls performed static standing tasks.
  • Spine kinematics and center of pressure data were recorded during standing trials.
  • A deep learning network with long short-term memory units was employed for classification.

Main Results:

  • The DNN model accurately recognized LBP populations with up to 97.2% precision and 97.2% recall.
  • Performance evaluation included accuracy, precision, recall, F1 measure, ROC, and AUC.
  • The model incorporating C7 sensor data demonstrated superior performance.

Conclusions:

  • Deep neural networks show significant potential for detecting LBP populations.
  • Kinematics and balance data can be effectively used by AI for LBP classification.
  • This AI-driven approach offers a promising tool for identifying individuals with low back pain.